Transfer of technologies between countries and
regions would widen the choice of options at the regional level, and
economies of scale and learning will lower the costs of their adoption.

7.10

Adequate human and organizational
capacity at every stage can increase the flow, and improve the quality,
of technologies transferred within and across countries. The transfer
of environmentally sound technologies has come to be seen as a major element
of global strategies to achieve sustainable development and climate change
mitigation. The local availability of technical, business, management,
and regulatory skills can enhance the flow of international capital, helping
to promote technology transfer. Technical skills are enhanced by the creation
of competence in associated services, organizational know-how, and capacity
improvement to formulate and enforce regulations. Capacity building is
a continuous process that needs to keep up with the evolution of mitigation
options as they respond to technological and social changes.

Governments through sound economic
policy and regulatory frameworks, transparency, and political stability
can create an enabling environment for private- and public-sector technology
transfers. At the macro-level, actions to consider include reform
of the legal system, protection of intellectual property rights, open
and competitive markets, reduced corruption, discouragement of restrictive
business practices, reform of export credit, political risk insurance,
reduction of tied aid, development of physical and communications infrastructure,
and improvement of macro-economic stability. At the sectoral and project
levels, actions include fuel and electricity price rationalization, energy
industry institutional reform, improving land tenure, transparent project
approval procedures, ensuring assessment of local technology needs and
social impact of technologies, cross-country R&D on innovative technologies,
and demonstration programs.

Networking among private and public
stakeholders, and focusing on products and techniques with multiple ancillary
benefits that meet or adapt to local development needs and priorities
foster effective technology transfer. National systems of innovation
(NSI) can help achieve this through activities such as (a) strengthening
educational institutions; (b) collection, assessment, and dissemination
of technical, commercial, financial, and legal information; (c) technology
assessment, demonstration projects, and extension services; (d) supporting
market intermediary organizations; and (e) innovative financial mechanisms.
Increasing flows of national and multilateral assistance can help to mobilize
and multiply additional financial resources, including official development
assistance, to support NSI activities.

For participating countries, an increasing
scale of international cooperation, such as emissions trading14
and technology transfer, will lower mitigation costs.

Box
7-1: Bottom-up and top-down approaches to cost estimates:
critical factors and the importance of uncertainties.

For a variety of reasons, significant differences and uncertainties
surround specific quantitative estimates of mitigation costs.
Cost estimates differ because of the (a) methodology used in the
analysis, and (b) underlying factors and assumptions built into
the analysis. Bottom-up models incorporate detailed studies of
engineering costs of a wide range of available and anticipated
technologies, and describe energy consumption in great detail.
However, they typically incorporate relatively little detail on
non-energy consumer behavior and interactions with other sectors
of the economy. The costs estimated by bottom-up models can range
from negative values (due to the adoption of "no-regrets"
options) to positive values. Negative costs indicate that the
direct energy benefits of a mitigation option exceed its direct
costs (net capital, operating, and maintenance costs). Market
and institutional barriers, however, can prevent, delay, or make
more costly the adoption of these options. Inclusion of implementation
and policy costs would add to the costs estimated by bottom-up
models.

Top-down models are aggregate models of the economy that often
draw on analysis of historical trends and relationships to predict
the large-scale interactions between sectors of the economy, especially
the interactions between the energy sector and the rest of the
economy. Top-down models typically incorporate relatively little
detail on energy consumption and technological change. The costs
estimated by top-down models usually range from zero to positive
values. This is because negative cost options estimated in bottom-up
models are assumed to be adopted in both the baseline and policy
scenarios. This is an important factor in the differences in the
estimates from these two types of models.

The inclusion of some factors will lead to lower cost estimates
and others to higher estimates. Incorporating multiple greenhouse
gases, sinks, induced technical change, and emissions trading
can lower costs. Further, studies suggest that some sources of
greenhouse gas emissions can be limited at no or negative net
social cost to the extent that policies can exploit no-regret
opportunities such as correcting market imperfections, inclusion
of ancillary benefits, and efficient tax revenue recycling. International
cooperation that facilitates cost-effective emissions reductions
can lower mitigation costs. On the other hand, accounting for
potential short-term macro shocks to the economy, constraints
on the use of domestic and international market mechanisms, high
transaction costs, inclusion of ancillary costs, and ineffective
tax recycling measures can increase estimated costs. Since no
analysis incorporates all relevant factors affecting mitigation
costs, estimated costs may not reflect the actual costs of implementing
mitigation actions.

A large number of studies using both top-down and bottom-up
approaches (see Box 7-1 for definitions) report on the costs of greenhouse
gas mitigation. Estimates of the costs of limiting fossil-fuel greenhouse
gas emissions vary widely and depend on choice of methodologies, underlying
assumptions, emissions scenarios, policy instruments, reporting year,
and other criteria.